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Record W2072070619 · doi:10.1080/14427591.2009.9686642

Linking occupation and place in community health

2009· article· en· W2072070619 on OpenAlexaff
Elizabeth Townsend, Sharon Dale Stone, Tanya Angelucci, Melissa Howey, Dawn Johnston, Sharon Lawlor

Bibliographic record

VenueJournal of Occupational Science · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsVancouver General HospitalLakehead UniversityDalhousie University
Fundersnot available
KeywordsMandateSociologyWork (physics)Social determinants of healthPublic relationsPolitical scienceEconomic growthHealth careEconomicsLaw

Abstract

fetched live from OpenAlex

Although the links between occupation and health have been studied historically for some time, little attention has been paid to the links between occupation and place. This article examines links between occupation and place: we argue that to promote inclusive social participation in all occupations, not limited to work, communities need to organize health services with a policy mandate and funding for addressing what people do and the social determinants that influence what they do in particular places. This claim is developed with reference to four vignettes that show the marginalizing influences on social participation when key social determinants of health are problematic – when transportation is inaccessible, weight loss and exercise programs are unaffordable, employment support is minimal, and there is insufficient low cost housing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.452
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2009
Admission routes1
Has abstractyes

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